To answer this question, we need to understand the concept of Star Schema in data warehousing.

In a Star Schema, one or more dimension tables are connected to a central fact table. The dimension tables contain descriptive attributes that provide context and details about the data in the fact table.

Now, let's go through each option to understand why it is correct or incorrect:

Option A) Short and Fat - This option is correct. In a Star Schema, the dimension tables are typically designed to be short and fat. This means that they have a wide structure with a larger number of columns, each representing a specific attribute or characteristic of the data.

Option B) Long and Thin - This option is incorrect. In a Star Schema, the dimension tables are not designed to be long and thin. This terminology suggests a narrow structure with fewer columns, which is not typically the case in a Star Schema design.

Option C) Long and Fat - This option is incorrect. Although the term "fat" is used correctly to describe the structure of dimension tables in a Star Schema, the term "long" is not accurate. Dimension tables are not typically designed to be long in terms of the number of rows or records they contain.

Option D) Short and Thin - This option is incorrect. The term "thin" suggests a narrow structure with fewer columns, which is not typically the case in a Star Schema design. Dimension tables are designed to be short and fat, as explained in Option A.

The correct answer is Option A) Short and Fat. This option is correct because it accurately describes the structure of dimension tables in a Star Schema.

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To answer this question, let's go through each option to understand why it is correct or incorrect:

Option A) Self Join - This option is incorrect because a self join is used when a table is joined with itself, not when generating a report using Drill Across.

Option B) Inner Join - This option is incorrect because an inner join is used to select records that have matching values in both tables being joined. While it can be used in some cases to generate a report, it is not specifically used for Drill Across.

Option C) Outer Join - This option is correct because an outer join is used to select records from one table, even if there are no matching records in the other table. When generating a report using Drill Across, an outer join is often used to include all the records from one table and the matching records from the other table.

Option D) Both 1 & 2 - This option is incorrect because neither the self join nor the inner join is specifically used for Drill Across. Only the outer join is commonly used for this purpose.

The correct answer is C) Outer Join. This option is correct because an outer join is commonly used to generate a report using Drill Across, including all the records from one table and the matching records from the other table.

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To answer this question, you need to understand the concept of data in Data Warehousing.

Option A) Normalized - Normalization is the process of organizing data in a database to eliminate redundancy and improve data integrity. In a normalized database, data is structured into separate tables with relationships defined between them. However, in data warehousing, normalization is not typically used because it can hinder performance and make querying complex.

Option B) Denormalized - Denormalization is the process of combining normalized tables into fewer tables or even a single table to improve performance and simplify querying. In data warehousing, denormalization is commonly used to optimize query performance and facilitate reporting and analysis.

Option C) None - This option is correct. In general, data in data warehousing is not normalized but is rather denormalized to enhance performance and facilitate analysis.

Option D) All of the above - This option is incorrect because the correct answer is option C, "none." Data in data warehousing is not typically normalized but is denormalized.

The correct answer is C, "none." This option is correct because data in data warehousing is not normalized but is instead denormalized to improve performance and facilitate analysis.

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To answer this question, let's go through each option to understand why it is correct or incorrect:

Option A) First level data mart - This option is incorrect because a consolidated data mart is not the same as a first-level data mart. A first-level data mart typically contains detailed data for a specific department or function within an organization.

Option B) Second level data mart - This option is correct because a consolidated data mart is a type of second-level data mart. A second-level data mart combines data from multiple first-level data marts to provide a broader and more comprehensive view of the organization's data.

Option C) All of these - This option is incorrect because a consolidated data mart is not the same as a first-level data mart. It is a type of second-level data mart.

Option D) None of Above - This option is incorrect because the correct answer is option B.

The correct answer is B) Second level data mart. This option is correct because a consolidated data mart is a type of second-level data mart.

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To answer this question, you need to understand the concept of datamarts and the term "stovepipe."

A datamart is a subset of a data warehouse that is focused on a specific area or department within an organization. It contains a collection of data that is relevant to that specific area.

The term "stovepipe" in the context of datamarts refers to the isolation of data within a specific datamart. It means that the data in the datamart is not easily accessible or shared with other datamarts or systems. This isolation can lead to data duplication, inconsistency, and limited integration with other parts of the organization.

Therefore, the correct answer is:

B) Isolated data - This option is correct because "stovepipe" in the context of datamarts refers to the isolation of data within a specific datamart.

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To answer this question, we need to understand the 4-step dimensional process and identify where declaring the grain of the business process occurs.

The 4-step dimensional process is a methodology used in data warehousing to design and build a data model. The steps involved are:

1. Identify the business process: This step involves understanding the key business processes and identifying the one that will be the focus of the data model.

2. Declare the grain: This step involves determining the level of detail at which the data will be captured and analyzed. The grain represents the lowest level of detail in the data model.

3. Identify the dimensions: This step involves identifying the different aspects or dimensions that will be used to analyze the data. Dimensions are the different ways in which data can be sliced and diced.

4. Identify the facts: This step involves identifying the numeric measures or facts that will be captured and analyzed in relation to the dimensions.

Based on the above information, we can conclude that declaring the grain of the business process occurs in the second step of the 4-step dimensional process.

Therefore, the correct answer is B) Second Step.

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To answer this question, let's go through each option to understand why it is correct or incorrect:

Option A) Transaction Number, bill of lading number, invoice number may be degenerate dimensions (DD) - This option is correct. Degenerate dimensions are those attributes that are not associated with any dimension table but are still important for analysis. Transaction Number, bill of lading number, and invoice number are examples of degenerate dimensions that can be used for analysis purposes.

Option B) Degenerate dimensions have no attributes - This option is incorrect. Degenerate dimensions can have attributes that are relevant to the analysis being performed. For example, a transaction number may have attributes such as transaction date, transaction amount, etc.

Option C) Degenerate dimensions do not join to actual dimension table - This option is correct. Degenerate dimensions are not joined to any dimension table because they do not have a relationship with any dimension. They are standalone attributes that are used for analysis.

Option D) All of the above are correct - This option is correct. All the statements mentioned in options A, B, and C are correct. Degenerate dimensions can include attributes such as transaction number, bill of lading number, and invoice number. They do not have any attributes of their own and do not join to any dimension table.

Therefore, the correct answer is D) All of the above are correct.

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To answer this question, you need to understand the concept of a Data Warehouse and the quality of data stored in it.

A Data Warehouse is a large, centralized repository that stores data from various sources for reporting and analysis purposes. The primary goal of a Data Warehouse is to provide accurate and reliable data for decision-making.

Option A) Clean Data - This option is correct because the data in a Data Warehouse is generally clean. Before data is loaded into a Data Warehouse, it goes through a process called Extract, Transform, and Load (ETL). During the transformation phase of ETL, data is cleaned, standardized, and validated to remove any inconsistencies or errors.

Option B) Dirty Data - This option is incorrect because the data in a Data Warehouse is not generally dirty. The ETL process ensures that data is cleaned and validated before being loaded into the Data Warehouse.

Option C) Clean and Dirty Data - This option is incorrect because the data in a Data Warehouse is primarily clean. While it is possible for some dirty data to exist in a Data Warehouse, it is not the norm. The ETL process aims to eliminate dirty data as much as possible.

Option D) None of the above - This option is incorrect because the correct answer is option A, which states that the data in a Data Warehouse is generally clean.

The correct answer is option A. The data in a Data Warehouse is generally clean because it goes through a rigorous ETL process to ensure its accuracy and reliability.

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